The sensitivity of ETK to training set size was tested by performing 50-fold cross-validation with training sets for pronunciation distance on U.S. names of sizes in {48, 96, 191, 381, 762, 1524, 3047} drawn from the 3047 equivalence classes in the 5000 U.S. names with pronunciation distance and k = 1.

despite Soundex's has many well-known limitations, including inability to handle different first letters with identical pronunciations (e.g., Soundex of "Kris" is K620, but Soundex of "Chris" is C620), truncation of long names, and bias towards English pronunciations, Soundex is still in use in many law enforcement and national security applications (dizard, 2004).


IGs of a single word are connected to each other deterministically using dependency links labeled DERIV, referred to as word-internal dependencies in the following, and the FORM and the LEMMA fields may be empty (they contain underscore characters in the data files).

the most common inference technique in transitionbased dependency parsing is greedy deterministic search, guided by a classifier for predicting the next transition given the current parser state and history, processing the tokens of the sentence in sequential left-to-right order 7 (Hall et al., 2007a;Mannem, 2007;Marinov, 2007;Wu et al., 2007).


Carreras (2007) extends the first-order model to incorporate a sum over scores for pairs of adjacent arcs in the tree, yielding a second-order model.

the LAS over all languages ranges from 80.32 to 54.55.


Arabic The analytical syntactic annotation of the Prague Arabic Dependency Treebank (PADT) (Hajič et al., 2004) can be considered a pure dependency annotation.

for models that take global properties of the tree into account, they cannot be applied.


Hence, children of the root node in the final output may not have been predicted as such by the treebank-induced classifier.

the labeled attachment score is below 80% for Arabic, Basque, Czech, Greek, Hungarian, and Turkish.


The current state is connected to previous states using a set of 7 distinct relationships defined in terms of each state's parser configuration, which includes of a stack and a queue.

we used the base feature model defined in (Nivre et al., 2006) for all the languages but Arabic, Chinese, Czech, and Turkish.


We used the pseudo-projective transformation introduced in (Nivre and Nilsson, 2005) to cast non-projective parsing tasks as projective.

as long as there exists some chain of relationships between any two states, then any statistical dependency which is clearly manifested in the data can be learned, even if it was not foreseen by the designer.


Phrase pairs that cannot stand on their own by demonstrating a certain level of significance are suspect and removing them from the phrasetable may be beneficial in terms of reducing the size of data structures.

even more surprising, pruning can actually raise the BLeU score although this phenomenon is less prominent if state of the art smoothing of phrasetable probabilities is employed.


Recent work in discriminative learning for many natural language tasks, such as part-of-speech tagging and information extraction, has shown that feature engineering plays a critical role in these approaches.

if we assume that all data use vocabularies that can be represented using 32-bit integers, then our 27M word corpus can easily be represented in around 500MB of memory.


The partitioning procedure was document preserving, i.e., sentences belonging to the same document were guaranteed to be assigned to the same partition.

we need a canonical way to build a large pool of diverse translation systems from a single translation engine.


With QE turned on, the applicability rate increases significantly to 60%.

hybrid transliteration models were described by Al-Onaizan and Knight (2002), and Oh et al.


So, it is likely such a Roman prefix has from one to four letters.

the derived probabilities embody a number of relationships: (a) Phoneme to syllable relationships (e.g., "b" vs. " 布 " as in "Brooklyn" and " 布 魯 克 林"(Bulukelin)), (b) Syllable to syllable relationships (e.g., "bu" vs. "布"), (c) Phonics rules (e.g., "br-" vs. "布" and "克" vs. "cl-").


Table 5: Improvement in SENSEVAL-3 WSD performance using our average-link agglomerative clustering vs. random clustering at the same granularity We have presented a classifier for automatic sense merging that significantly outperforms previously proposed automatic methods.

this work was supported in part by the Disruptive technology Office (DtO)'s Advanced Question Answering for Intelligence (AQUAINt) Phase III Program.


Clearly any amount of clustering will only increase WSD performance.

finally, we evaluate the sense-clustered taxonomies we create on the problem of providing improved coarse-grained sense distinctions for WSD evaluation.


Classification in this approach is done using the full Bayesian network just as in the EM approach.

our approach tries to address this problem by clustering features to relieve the scarcity problem, specifically on the bag-of-words feature.


As most errors were attributable to the hyponomy structure of WORDNET, incorporating the novel use of topic modeling presented here with a more mature unsupervised WSD algorithm to replace the underlying WORDNET-WALK could lead to advances in stateof-the-art unsupervised WSD accuracy.

word sense disambiguation (wSD) is the task of determining the meaning of an ambiguous word in its context.


For example, "maturity" in topic two in Table 2 is sandwiched between "foot" and "center," both of which occur about five times more than "maturity."

it is a distribution over words and thus can be integrated into any generative model of text, such as topic models.


Of course, the information coming from Figure 1 is not sufficient for our purposes.

the top 30 MWE candidates are used in this experiment.


Only those predictions that occurred 5 times or more were taken into consideration for the generation of the new lexical entries.

the task of automatically identifying Multiword Expressions (MWEs) like phrasal verbs (break down) and compound nouns (coffee machine) using statistical measures has been the focus of considerable investigative effort, (e.g.


Unfortunately, the construction of these resources is the manual result of human efforts and therefore likely to contain errors of omission and commission (Briscoe and Carroll, 1997).

to provide a unified account for the detection and encoding of these distinct but related phenomena is a real challenge for NLP systems.


This is followed by a detailed investigation and comparison of measures for MWE identification ( § 5).

from the perspective of grammar engineering, even with a good candidate list of MWEs, great effort is still required in order to incorporate such word units into a given grammar automatically and in a precise way.


A final type of feature we added was based on the behavior of nouns, adjectives and verbs in each domain.

when we examine the coarse POS tags, which do not distinguish between nouns, these differences disappear.


• FRQ: This system picks the most frequently mentioned party in a message as the predicted winning party.

predictive data mining is only feasible when a large amount of structured numerical data (e.g., in a database) is available.


They learn MSA in school, and have no native-speaker intuition about case.

we make use of all dash-tags provided by the ATB as arc labels and we extend the label set to explicitly mark objects of prepositions (POBJ), possessors in idafa construction (IDAFA), conjuncts (CONJ) and conjunctions (CC), and the accusative specifier, tamyiz (TMZ).


2005) which was trained (supervised) using GENIA corpus and uses a machine learning model more sophisticated than the simple first-order HMM tagger we use.

we used the kNN method to associate words in the new domain with possible POS tags.


It is quite common to use suffix information in the prediction of POS tags for occurrences of new words.

the best results (95.77%) were obtained for the case where we took the lexical probabilities directly from kNN using only RFDist and by discarding all tags assigned with probability less than 0.02.


The number of learning iterations over the training data, which is the only adjustable parameter of the algorithm, was determined by cross-validation.

certain languages, such as catalan, use detailed dependency labeling, that for instance distinguish between adverbials of location and time.


In both cases the selection criteria was to choose sentences shorter than 30 tokens.

training SVMs on large data sets like those arising from a big training corpus was too computationally expensive, forcing them to resort to partitioning the training data (by POS) and to learn several models.


We compared the performance of the parser with a parser based on local SVM classifiers (Johansson and Nugues, 2006).

between the two parses in a manner that makes the tree projective, single-head, rooted, and cycle-free, we applied the Eisner algorithm (Eisner, 1996).


We describe an incremental parser that was trained to minimize cost over sentences rather than over individual parsing actions.

the beam search allows us to find a reasonable approximation.


In his (2001) paper, Covington presents a "fundamental" algorithm for dependency parsing, which he claims has been known since the 1960s but has, up to his paper-publication, not been presented systematically in the literature.

the results we got from additional optimizations we performed on Hungarian, following recommendation from the anonymous reviewers, may lead to a different conclusion.


Experimental results show that the system score is better than the average score among the participating systems.

the result is very good as it is close to the top3 score of participating systems.


Due to the limitation of participation time, we only applied the first-order decoding parsing algorithm in CONLL-2007.

unlike maximum-margin parsing, it is not limited to parsing sentences of 15 words or less due to computation time.


Our system makes no provisions for non-projective edges.

in languages with subject-verb agreement, we added a nominative case feature to finite verbs.


Note that the edge is coming from the parent to the child.

the hardware used was an Intel CPU at 3.0 Ghz with 32 GB of memory, and the software was written in C++.


In training and adapting we found that bootstrapping based on unlabelled dependencies worked better in all cases than utilizing the labelled mapping we derived.

we chose to use Model G for the blind test as it incorporates most information from the in-   For the blind test submission we used Models G and H to parse the PCHEMTB and CHILDES data, respectively.


We plan to compare it with out method in the future.

this paper presents and analyzes the impact of two preprocessing components, namely, neighbor parsing and root-parsing.


The fact that the parser learns these labels without explicit annotation of the link between the arguments and the predicate to which they are assigned, but based on the smoothed representation of the derivation of the parse tree and only very limited lexicalisation, appears to confirm linking theory, which assumes a correlation between the syntactic configuration of a sentence and the lexical semantic labels.

belled with a semantic functional label in the original PTB receive the composite semantic role label AM-X, where X stands for labels such as LOC, TMP or ADV, for locative, temporal and adverbial modifiers respectively.


Since the assignment of the semantic role labels proper is performed during parsing, the number of nodes that require a semantic role is only 20% of the total.

differently from previous work that had found that global coherence constraints considerably improved performance (Punyakanok et al., 2005), using global filtering contraints showed no improvement in our learning model.


As one example, the cake spine shown above can be s-adjoined into the VP node of the ate spine, to form the tree shown in figure 1(a).

4 We will al-low multiple modifier spines to s-adjoin or r-adjoin into the same node in a head spine; see section 2.3 for more details.


We map the confidence value of each classifier into the range of 0 to 1 with sigmoid function 7 , and select a tag sequence which maximizes the sum of those log values by Viterbi search.

most of these previous works manually selected combination of features except for SVm with polynomial kernel and (Kudo and matsumoto, 2001) a boosting-based re-ranking (Kudo et al., 2005).


Malt-Parser has a wide range of parameters that need to be optimized when parsing a new language.

we would therefore expect postverbal subjects and preverbal objects to be more dominant among the errors than in the treebank as a whole (23% and 6% respectively).


A set of corresponding experiments with automatic features gave significant improvement from the addition of individual features and a cumulative effect of the same features in combination.

there is also syncretism in the pronominal paradigm where the pronoun is invariant for case, e.g.


Some other semantic parser learning systems which need MRL in the form of Prolog (Tang and Mooney, 2001) or λ-calculus (Zettlemoyer and Collins, 2007;Wong and Mooney, 2007) do not use productions of the MRG but instead use predicates of the MRL.

this operator will not eliminate other occurrences of t 1 and t 2 on the RHS of other productions in which they do not occur adjacent to each other.


(2003), Bannard (2005) and Fazly and Stevenson (2006)).

in order to answer the first question, we use two assumptions.


The model then revises the meaning of the words in U (t) by incorporating the alignment probabilities for the current input pair.

moreover, the overall behaviour of our model confirms that the probabilistic bootstrapping approach to word learning naturally leads to the onset of fast mapping in the course of lexical development, without hard-coding any specialized learning mechanism into the model to account for this phenomenon.


As discussed in the previous section, results from the human experiments as well as our computational simulations show that the referent of a novel target word can be selected based on the previous knowledge about the present objects and their names.

upon hearing ball, the model can confidently retrieve its meaning ball, which is the one with the highest probability p(m|ball ) among all possible meanings m. In such a case, if ball is present in the scene, the model can easily pick it as the referent of the familiar target name, without processing the other objects in the scene.


We simulate this condition by using the same input items for referent selection trials as in the previous simulation, but we replace the retention trial with the following: The third object, lukk, has not been seen by the model before.

the meaning probability p(m|w) is used to retrieve the most probable meaning for w among all the possible meaning symbols m. in the referent selection tasks performed by children, the subject is often forced to select the referent of a target word from among a limited set of objects, even when the meaning of the target word has not been accurately learned yet.


Gender knowledge is represented as a set of rules containing exceptions.

for instance, suppose you are exposed to only 4 words A, B, C and D with frequencies .5, .25, .125, .125.


WordNet does not give an order of generality inside synsets.


they are in fact important because they allow to structure information, thus fostering their search and reuse.


These results also show that the weighting of the graph plays an important issue in our methodology.

we deeply believe that asymmetry is a key factor for discovering the degree of generality of terms.


This technique could be applied to hypernym/hyponym relationships although the authors do not mention it.

works done by Snow et al., 2006;Sang and Hofmann, 2007;Bollegala et al., 2007) have proposed methodologies to automatically acquire these patterns mostly based on supervised learning to leverage manual work.


Another method, by (Leacock et al., 1998), normalizes path distance based on the depth of hierarchy.

the second assumption reflects the information provided by context selectors, for which we use a relatedness measure to compare with the original word.


Acquire probabilities of selectors occurring for all nouns, verbs, adjectives, pronouns and proper nouns from the Web.

our similarity measure examines the use of links in WordNet, and attempts to deal with the uniformity problem by normalizing depths based on average leaf node depth.


Essentially, human annotation combines two tasks in one: deciding where each phrase goes in the layout, and deciding which words within a phrase can be depicted with icons.

to design such a layout, we conducted a pilot study.


We trained our CRF models using the MAL-LET toolkit (McCallum, 2002).

non-native speakers do not have the same degree of built-in English syntactic knowledge.


Unfortunately, it is not this simple, as Prop-Bank roles are verb-specific.

native speakers do not get as many syntactic hints.


In the latter, a probabilistic approach is used for harvesting prefixes and suffixes in Finnish words, favoring the longer ones.

for example, the suffix -eto cannot be applied on its own, but must always be used in conjunction with the prefix e-, to form words such as elúeto, as shown in Table 4.


Using only a 2-word reordering window the score of the original Arabic translations(57.92) was comparable to the best score (58.31) obtained by using a 4-word reordering window.

we carried out experiments from monotone decoding up to 9 word distance reordering limit for the two development sets IwSLT04 and MT03.


Since we design syntactic transformation models using un-lexicalized synchronous CFGs, the number of rules is small 1 .

we consider an example of a syntactic tree and a simple CFG extracted from that tree.


The training data includes 319 referring expressions in the furniture domain and 274 in the people domain.

a fully minimized permutation automaton has only a single initial and final state.


To choose previous relevant decisions, we make use of the partial syntactic and semantic dependency structures which have been decided so far in the parse.

the only constraints we place on the set of semantic dependencies are imposed by the use of a stack, which excludes crossing arcs.


For parsers that consider features of single links only, the Chu-Liu/Edmonds algorithm can be used instead.

table 4 shows the effect of adding second-order features to the parser in terms of accuracy as well as training and parsing time on a Mac Pro, 3.2 GHz.


The use of trace labels in the pseudo-projective transformation leads to a proliferation of edge label types: from 69 to 234 in the training set, many of which occur only once.

this algorithm cannot be generalized to the second-order setting - McDonald and Pereira (2006) proved that this problem is NPhard, and described an approximate greedy search algorithm.


, l sem pq one for each sentence predicate p i .

in this example, the correct output for the dependency previously-announced is h = announced, m = previously, l syn = AMOD, l sem p 1 = null, l sem p 2 = AM-TMP, l sem p 3 = null.


Otherwise picking the wrong candidate would increase overall score and no role features can re-ject this decision because the corresponding structural constraints are missing.

for instance, the formula lemma(p, +l) ∧ ppos(a, +p) ∧hasRole(p, a) ⇒ sense(p, +f ) figure 2: Markov Network that illustrates the structural constraints we use.


For argument, a dependency version of the pruning algorithm in (Xue and Palmer, 2004) is used to find, in an iterative way, the current syntactic head and its siblings in a parse tree in a constituentbased representation.

x.dprelPath and x.posPath return the PoS tag sequence from x to the beginnings of SharedDprelPath and Shared-PosPath, respectively.


Despite the simplicity of this approach, our system has produced promising results.

we specify the first word in a word pair as a predicate candidate (i.e., a semantic head, and noted as p in our feature representation) and the next as an argument candidate (i.e., a semantic de- c preddir: the direction to the current predicate candidate.


Table 3 also shows that the unbalance between precision and recall is higher for dependencies of nouns than for dependencies of verbs, and that both recall and precision are higher for dependencies from verbs.

we are grateful to Stefan Becuwe for his support.


model was implemented in the closed challenge and the results are shown in table 5.

this implies that the error of syntactic pars-ing and predicate tagging could be probably augmented in semantic dependent labeling.


It should be cleared that the ordinal numbers are only used to distinguish different meanings of a predicate.

the binary model can't distinguish different rolesets of predicate, but can identify which words are predicates in sentences.


The experiments on development data show that relabeling process is helpful, which improves the    The semantic dependency parsing component is based on the last syntactic dependency parsing component.

we rewrote it with C++ which can manage the memory more exactly.


The "argmax problem" in structured prediction is not tractable in the general case.

specially, using a pruning algorithm, we collect a set of argument candidates; then we classify dependency relations between argument candidates and the predicates and predict whether a candidate is an argument.


Most of Previous SRL systems only consider given predicates.

in the remainder of this paper, we will briefly describe our system architecture, present various features used by our models and report the performance on CoNLL 2008 shared task (both official and unofficial).


This suggests that such difference between these two state-of-theart dependency parsers does not much affect corresponding SRL systems.

the first step of the shared task is to identify the verb and noun predicates in a sentence.


For each token i we extract the following features ( , stands for token combination): depreli,j, split lemmai, split lemmaj , ppossi, ppossj 7.

the ME classifier is considerably faster to train but has a lower accuracy: this model achieved an LAS of 76.49% on the development set.


We see larger drops when switching to predicted PIC (line 3): 5-6 labeled F 1 points in domain and 12 points out of domain.

the use of multiple parsers at the beginning of the process, and re-ranking at the end, contribute beneficial stochastic aspects to the system.


Our predicate identification approach is described in section 2.2.

as the search space of LR parser is exponential growth with the word number, the maximum size of candidate states is limited to 50.


Table 5 shows the overall results with different N. The results are improved when N changes from 1 to 2.

brown test set covers sections ck01, ck02, and ck03 of the brown corpus.


Another interesting thing to note is that all of improvement for noun predicates came from choosing the most common sense instead of always choosing sense 1.

as discussed above, there is a large amount of room for improvement for argument labeling of nouns.


We avoid unrolling this graphical model by using Markov-chain Monte Carlo for inference, and make inference more efficient with learned proposal distributions.

success in integrated, end-to-end natural language understanding remains elusive.


Previous works addressing training sample size vs. parser performance for constituency parsers (Section 2) evaluated training sample size using the total number of constituents (TC).

sentences with a large number of structures impose a special cognitive load on the annotator.


Table 1) in the partial data: it does not differ significantly from the 'trees (bi.)'

the increased level of noise in the fallback data has less (but significant) 8 impact on the graph-based counterpart (−2.68%).


Most state-of-the-art parsers for natural languages are data-driven and depend on the availability of sufficient amounts of labeled training data.

relaxing the reliability citerion, and-in the case of the transition-based MaltParser-also to aggressively filtering out all but the reliable complete trees.


The proportion of usable sentences is reduced to 2.11%.

1 In the experiments reported here, we use the language pair English-Dutch, with English as the source for projection (L 1 ) and Dutch as L 2 .


These systems are summarized in Table 3.

in combination with the fMalt/fMST systems ('bi+frags ≤3 (fMalt/fMST)'), both parsers significantly outperform the tree-  oriented models ('trees (bidirectional)') by 3.21% (Malt) and 2.26% (MST).


Results with gold-standard nega-tion signals are especially better for the clinical subcorpus because except for lack, negative and not, all negation signals score a PCS higher than 90 %.

the systems mentioned above are essentially based on lexical information.


Many of the pattern candidates discovered in the previous stage are not usable.

in comparison, a state-of-art supervised parser (Charniak and Johnson, 2005) would process the same amount of data in 1.3 years 7 .


The utilized approach to concept discovery is based on connectivity structures in the all-pattern term relationship graph G, resulting from merging all of the single-pattern graphs for symmetric patterns selected in the previous stage.

it completely ignores potentially useful syntactic or morphological information.


An example of a positive test item is supersede collection.

(5)), and it incorporates competition between categories by storing both positive and negative exemplars and categorizing according to the majority among the k nearest neighbors.


7 The distributed model surpasses the centered one on this dataset.

i can reasonably be expected to be negative, since a negative β IN i indicates that membership of a point x in the w-region gets less likely as the distance (w i −x i ) 2 increases.


Since the first two pronouns in each pronoun template belong to the first clause (C1), and the last two to the second clause (C2), the templates can be re- , with the restriction that partition 3 should not contain any of the four pronouns in the template.

here the two eventualities are like(she, he) and help(he, she).


For example, "help" and "give" could both be reciprocated by either "thank" or "like".

an example of an instance matched by one such pattern is ", I cooked dinner for her and she loves me for that ."


"), we determine the order through clues such as the relative temporal ordering of the verbs as determined by their tense (e.g., past or present tense happens before future tense) and whether the verbs denote an action (e.g., "to chase") or a state (e.g., "to love").

noun and pronoun coreference should also be resolved at this point.


A simple heuristic collapsed compound or sequential nouns to their final noun, an approximation of the head noun of the noun phrase.

the NPattern feature indicates how many nouns there are in the sentence and which noun the target is.


This inference is implemented using a beam search that looks at arguments in a fixed order and roles from A0 up.

when this largely unsupervised training approach is applied to a corpus of child directed speech, the BabySRL learns shallow structural cues that allow it to mimic striking behaviors found in experiments with children and begin to correctly identify agents in a sentence.


Our loss is a sentence-level approximation of (1 − BLEU).

mCmC probabilistically generates sample derivations from the complete search space.


The experiments in this section were performed using the French-English and German-English parallel corpora from the WMT09 shared translation task (Callison-Burch et al., 2009), as well as 300k parallel Arabic-English sentences from the NIST MT evaluation training data.

for applications which truly do require a distribution over translations, such as re-ranking, our method for minimising expected risk would be the objective of choice.


We group nouns because there is a strong tendency for nouns to have only one sense (and hence gender) per discourse.

like others, they consider gender and number separately, only learning statistical gender for the masculine, feminine, and neutral classes.


While gender has been shown to be the key feature for statistical pronoun resolution (Ge et al., 1998;Bergsma and Lin, 2006), it remains to be seen whether the exceptional accuracy obtained here will translate into improvements in resolution performance.

a method of back-off to help classify unseen nouns: For multi-token nouns (like Bob Johnson), we also include the four gender counts aggregated over all nouns sharing the first token (Bob .


We can regard the Bergsma and Lin (2006) approach and our discriminative system as two orthogonal views of gender, in a co-training sense (Blum and Mitchell, 1998).

the CONTEXT-AUTO predictions improve accuracy, as these scores provide orthogonal and hence helpful information for the semi-supervised classifier.


6 Further Extensions: Generalizing to other word types via tagset mapping Most of the previous literature on this problem focuses on evaluating on nouns (Rapp, 1999;Koehn and Knight 2002;Haghighi et al., 2008).

we found that cosine similarity gave the best results in our experimental conditions.


When the same token type t appears in several locations in the text, say x i 1 , x i 2 , .

examining the results, we reach two conclusions: (1) choice of encoding scheme has a big impact on the system perfor-mance and (2) the less used BILOU formalism significantly outperforms the widely adopted BIO tagging scheme.


NER system should be robust across multiple domains, as it is expected to be applied on a diverse set of documents: historical texts, news articles, patent applications, webpages etc.

webpages -we have assembled and manually annotated a collection of 20 webpages, including personal, academic and computer-science conference homepages.


(2008) are supervised, performing semantic analysis of the parse tree and gold standard-based calssification, respectively.

second, POs statistics about the constituents in I are collected.


A possible answer to this problem can be provided by high quality parses produced by unsupervised parsers that require little to no manual efforts for their training.

the SEPA algorithm of Reichart and Rappoport (2007), an algorithm for supervised constituency parsers, can be applied to unsupervised parsers in a way that preserves the unsupervised nature of the selection task.


The characteristics of the induced clusterings are shown in Table 2 5 .

in addition, V does not have many of the attractive properties of Vi.


NVI is not symmetric since the term in its denominator is H(C), the entropy of the correct class assignment.

we thus expect MDL+SC to provide better clustering than MDL; a good clustering evaluation measure should reflect this expectation.


Zhao and Karypis (2001) define Purity and Entropy as follows: where q is the number of classes, k the number of clusters, n r cluster r's size, and n i r is the number of elements in class i assigned to cluster r. Both measures are good measures for homogeneity (Purity increases and Entropy decreases when homogeneity increases).

as noted in (Rosenberg and Hirschberg, 2007), these measures evaluate not only the quality of the proposed clustering but also of the mapping scheme.


When the number of classes increases, the numerator decreases and the denominator increases, and hence the score decreases.

evaluating the results of a clustering algorithm is hard.


Patterns are defined as dependency paths with at most three intermediate nodes between the two focus nouns.

while no automatic text preprocessing method is free of errors, it is indeed true that approaches other than full parsing, like for example shallow parsing, offer useful information at a considerably cheaper processing cost.


We have chosen the approach suggested by : remove all nouns with multiple senses from the data set and use only the monosemous words for finding good extraction patterns.

for newspaper data, lexical patterns outperform dependency patterns both for precision and f β=1 .


One is to automatically assign senses to words.

collect from a text corpus phrases (consecutive word sequences from a single sentence) that contain a pair of nouns 2.


We mark the first phrase as a hypernym phrase (color is a hypernym of cyan) while the second is marked as non-hypernym (color is not a hypernym of bird).

we are unable to find major differences in the results of the two approaches.


One possible explanation lies with the linguistic features with which we approached CoNLL: these are the same ones we originally developed for MUC-6.

because a typical entity extraction model can use tens or hundreds of thousands of features, it is not practical to displace every one of them.


We used the Carafe open-source implementation of sequence-based conditional random fields.

what we duplicate is the indicator function for a feature.


By using a small number of dimensions, we can define a very compact representation of the proximity matrix and, consequently, reduce the memory requirements while preserving most of the information.

for example, quantum mechanics, atomic physics, and Nobel Prize in physics are all terms that bound Niels Bohr and Enrico fermi to the concept of physics.


For example, in the sentence "Prof. Enrico Fermi discovered a way to induce artificial radiation in heavy elements by shooting neutrons into their atomic nuclei," single-context features such as, the prefix Prof. and the capital letters, provides enough evidence that Enrico Fermi is a person and a professor.

although this benchmark is still far from being "large", it allows drawing more valid conclusions than past ones.


As a result, we obtain a generalized similarity function between multi-contexts that incorporates semantic relations between terms, automatically learned from unlabeled data.

finally, the latent semantic kernel is explicitly defined as follows where φ is the mapping defined in Equation 2 and π is the linear transformation defined in Equation 6.


system concerned with the automatic annotation of documents with Wikipedia links (Mihalcea and Csomai, 2007).

the system has the ability to identify possible relevant alternative topics using the comprehensive catalog of Wikipedia computer science articles and their possible associations.


A total of 20 systems participated in the closed challenge; 13 of them in the Joint task and seven in the SRL-only task.

we compute macro precision and recall scores by averaging the labeled precision and recall for semantic dependencies with the LAS for syntactic dependencies: 3 where LM P is the labeled macro precision and LP sem is the labeled precision for semantic dependencies.


We considerably simplified the annotations provided by SALSA, which use a rather complex annotation scheme.

• Proposition Bank I (PropBank) -The Prop-Bank annotation  classifies the arguments of all the main verbs in the Penn Treebank corpus, other than be.


It is perhaps not surprising that most of the observations from the 2008 shared task still hold; namely, the best systems overall do not use joint learning or optimization (the best such system was placed third in the Joint task, and there were only four systems where the learning methodology can be considered "joint").

the main difference between the 2008 and 2009 version of the corpora is the generation of word lemmas.


For the out-of-domain evaluation data, part of the Czech side of the Prague Czech-English Dependency Treebank (version 2, under construction) was used 13 , see also (Čmejrek et al., 2004).

since the results might have been influenced by the supplied parser, separate ranking is provided for both types of the systems.


It is sampled from the EUROPARL corpus and was chosen to maximize the lexical coverage, i.e., it contains of a large number of infrequent predicates.

they did not have to determine which tokens are predicates (or more precisely, which are the argument-bearing tokens), since they were marked by 'Y' in the FILLPRED field.


The classifier will build a model directly and let "HEAD2" equal to "HEAD1".

the paper first describes how to carry out syntactic dependency parsing and semantic role labeling, and then a new iterative approach is presented for joint parsing.


The row of "Initial step" shows the results of this sub task in the original step.

this approach probably depends on the accuracy of the initial labeling results.


Because syntactic and semantic dependencies have been linked, the tree contains also semantic dependencies.

the features used by this classifier are: • the word, lemma, POS and FILLPRED of the token, of the combined token, and of two tokens before and after the token.


In our system this is achieved by fully merging semantic and syntactic dependencies at the word level as the first step.

the tree is missing the purely semantic dependencies.


Though FEAT features are also available for Japanese, we don't adopt them for this language due to the hight training cost.

as for Chinese and English, we continue to use a dependency version of the pruning algorithm of (Xue and Palmer, 2004) as described in (Zhao and Kit, 2008).


As we cannot identify a group of simple predicateargument relations from the syntactic tree.

for example, the intersection set from six languages, as Chinese is excluded, only includes one feature template, p.lemma (the lemma of the predicate candidate).


From the predicate or the argument to the syntactic root along the syntactic tree, the first verb(noun) that is met is called as the low support verb(noun), and the nearest one to the root is called as the high support verb(noun).

it is still inefficient for a singlestage argument identification/classification classification task.


We discarded large sentences during training for efficiency reasons.

we also omitted the definition of the domain of some variables.


The Catalan and Spanish datasets (Taulé et al., 2008) present two interesting properties.

the semantic dependency between a predicate and an argument always has a matching syntactic dependency between a head and a modifier.


Ivan Meza-Ruiz * Sebastian Riedel † ‡ Markov Logic (ML, Richardson and Domingos, 2006) is a Statistical Relational Learning language based on First Order Logic and Markov Networks.

a more detailed description of the formulae can be found in our MLN model files.


Clustering by lemma: all dependency trees having the same lemma as predicate are put together if the number of such trees is at least a 2 Remark: unless stated otherwise, we will use terms "lemma", "POS tag" "dependency relation" or "head" to refer to the information contained in the corresponding "P-columns" for each word.

in order to keep the system as generic as possible, features are generated automatically and only a few parameters may vary.


We introduce two factors: Local Factor F L and Global Factor F G defined as follows.

first, our system determines the word sense for each predicate with the predicate classifier, and then it detects the highest scored argument assignment using the argument classifier with global features.


The model is similar to re-ranking (Toutanova et al., 2005;Johansson and Nugues, 2008).

another research direction is investigating joint approaches.


They range from supporting statistical models to a few hard-coded rules.

tokens which were considered predicates were marked in the input data.


We did not attempt to gain advantage from training a joint classifier for all the subtasks.

some features of the parser are designed to work with Czech, or even with the Prague Dependency Treebank.


See Table 2 for some examples (ϑ = 0.9).

especially noun phrases and prepositional phrases are clustered into their respective classes.


Each possible structure must make four (out of six) adjacent stops, incurring identical probabilities: Thus, the MLE model does not break symmetry and rates each of the five parse trees as equally likely.

nevertheless, it would attain a higher per-token accuracy of 50%.


Feedback evaluates whether a logical form produces a response matching r: We are now ready to present our learning with feedback algorithms that operate in situations where input sentences, {x l } N l=1 , and a teacher feedback mechanism, Feedback , are available.

the key contributions of this paper are: We propose a new learning paradigm for learning semantic parsers without any annotated meaning representations.


DIRECT uses the signal directly to learn a binary classifier capable of replicating the feedback, whereas AGGRESSIVE learns a structured predictor that can repeatedly obtain the logical forms for which positive feedback was received.

learning is then defined over hidden patterns in the training data that associate logical symbols with lexical and syntactic elements.


Here we clearly need to identify sets of strings corresponding to the two parts of this language, but it is easy to see that no one context will suffice.

this can clearly be computed efficiently using a union-find algorithm, in time polynomial in |K| and |F |.


Since POS (Possessive ending) tends to be grouped with the POS tag on its left, it is the end of the constituent.

the method proposed in this work, that we expect to refine by assigning different probabilities to separators and sub-separators, depending on the context they appear in, provides a very promising approach.


By automatically annotating unlabeled bitexts with these bilingual models, we can train new monolingual models that do not rely on bilingual data at test time, but still perform substantially better than models trained using only monolingual resources.

we are training the bilingual view to match the output of these same models, which can be trivially achieved by putting weight on only the monolingual model scores and never recruiting any bilingual features.


First, at initialization, words are randomly assigned to clusters in a way that cluster sizes are distributed according to the Zipfian distribution (with a parameter s).

the right number in the parentheses is 100 times the difference between the score of our model and the mean score of 100 runs of Clark's (Ct).


VI and NVI induce the same order over clusterings but NVI values for good clusterings lie in [0, 1].

applications are not guaranteed to use an induced tagging of the quality reported for the algorithm.


The syntactic features are based on paths through the dependency tree.

since the boundaries of the spans annotated in the MPQA corpus are not strictly defined in the annotation guidelines , measuring precision and recall using exact boundary scoring will result in figures that are too low to be indicative of the usefulness of the system.


There are two problems in applying the bipartite graph technique to finding an M-1 mapping.

in this section, we briefly survey the three main types: mapping based, counting pairs, and information theoretic measures, and motivate our decision to focus on the first in this paper.


In NLP, clustering has been used for virtually every semi-and unsupervised task, including POS tagging (Clark, 2003), labeled parse tree induction (Reichart and Rappoport, 2008), verb-type classification (Schulte im Walde, 2006), lexical acquisition (Davidov and Rappoport, 2006;Davidov and Rappoport, 2008), multilingual document clustering (Montavlo et al., 2006), coreference resolution (Nicolae and Nicolae, 2006) and named entity recognition (Elsner et al., 2009).

since we are not aware of any exact method for solving this problem, we use a hill-climbing algorithm.


This is because, as explained above, the algorithms do not induce the same number of clusters and this affects their results.

the methodology of clustering evaluation is of great importance.


To preserve the nature of 1-1 mapping that punishes for excessive clusters 2 , we define |h(B i )| to be equal to |B i | even for these unmapped clusters.

clustering evaluation is of great importance.


The subtraction approaches presented in this work provide a basic algorithm for to handling segmentation of incoming data in an online fashion.

the Utterance baseline segmenter assumes every utterance is a single word.


Work in this tradition makes no claims, however, that these methods are actually the ones used by human learners.

while probabilistic memory means that the learner will fail to recognize words it has seen before, potentially decreasing recall, it also provides the learner the benefit of probabilistically failing to repeat previous mistakes if they occur rarely.


a test with the original non-deficient empty word model of the IBM-3 furthermore confirmed the impression of (Och and Ney, 2003) that overly many words are aligned to the empty word: the tendency is even stronger in the Viterbi alignments.


These three observations suggest that the hashgold standard is noisy (containing non-sarcastic tweets) and is biased toward the hardest (inseparable) forms of sarcasm where even humans get it wrong without an explicit indication.

seed and extended training sets (Amazon).


Pattern matching Once patterns are selected, we have used each pattern to construct a single entry in the feature vectors.

combining them in one tweet brings the sarcasm to its extreme.


We believe that this work makes a significant step towards learning synchronous grammars for SMT.

in a sense, the real baseline to which we might compare against should be a system employing the MLE estimate for the grammar extracted from the whole training corpus.


Our approach might also benefit from such a refinement but we do not attempt this problem here.

a well-known method for estimating generalisation error is k-fold Cross-Validation (CV) (Hastie et al., 2001).


The coverage criterion used by the preferred ordering algorithm in Section 3.1 ensures good correspondence between the rank of a sentence and its diversity, i.e.

this is a key departure from existing work on active learning for SMt.


As more and more features are added into the model, more data is needed for training.

variations include using random split feature sets or two different classification algorithms.


The original unlabeled corpus contains about 288 thousand sentence pairs.

because A I is decided before the supervised learning starts, it will be able to provide evidence for making decisions on candidate links.


In this paper, we studied three closed related Bayesian models for unsupervised sentiment detection, namely LSM, JST and Reverse-JST.

the procedure of generating a word in LSM starts by firstly choosing a distribution over three sentiment labels for a document.


Gibbs sampling is used to estimate the posterior distribution of LSM, as well as the JST and Reverse-JST models that will be discussed in the following two sections.

the above analysis reveals the effectiveness of JSt in extracting topics and capturing topic sentiment from text.


When comparing JST and Reverse-JST, it can be observed that Reverse-JST performed slightly worse than JST for all sets of experiments with about 1% to 2% drop in accuracy.

tSM may suffer from the problem of overfitting the data which is known as a deficiency of pLSI, and postprocessing is also required in order to calculate the sentiment prediction for a document.


The system tackles the effect of negations and quantifiers in sentiment analysis, and addresses the problem of word ambiguity, taken into account the contextual meaning of the terms in the text by using a word sense disambiguation algorithm.

to this aim, we define two intensity distributions: the 3classes and the 5-classes distribution.


Other words not identified with this dependency, but also with a negation meaning, such as no, none¸ nor or nobody, are identified using a negation token list.

our system reports promising results: using 5 classes it achieves better results than other participant in the SemEval task using just 3 classes (Chaumartin, 2007;Katz et al., 2007).


The sentiment analysis discipline in computational linguistic is mainly focused on identifying/classifying different emotional contents within a phrase, sentence or document.

after removing these synsets and those labeled with an emotional category not included in the 16-categories subset used in this work, the affective lexicon presents 798 synsets.


Group all of the noun phrase chunks that share a synset into a single coreference chain.

although the final coreference chain may be predicted by two different models, the synsets they use to do so may differ, affecting the synset and hypernym features used for ontological prediction.


5 Augmenting the attributes with sum counts (the + systems) strongly helps with fewer examples, especially in conjunction with the more efficient CS-SVM.

it is instructive to compare the CS-SVM in (3) to the unsupervised SUMLM approach in Bergsma et al.


Systems that can reliably identify these errors are needed in word processing and educational software.


More formally, we can regard the set of weights, w 1 , ..., w N , as the distribution of a discrete random variable, W .

as C approaches zero, variance minimization becomes the sole objective of (6), and uniform weights are produced.


Simple n-gram language models are known for providing quite a strong baseline for word prediction; for example, Brown et al.


This is done for each of the category sets described in Section 3.2.

the average score gained by part of speech tags is also lower than the one by our categories.


the best overall result was achieved with CRF, incorporat-  table 3: DA classification F-score with semantic features (t = "title Similarity", P = "Post Similarity", C = "Post Characteristics", and U = "User Profile"; above-baseline results in bold) ing structural features and title similarity, at an Fscore of 0.751.


The positive examples of a production become the negative examples for all those productions which have the same righthand-sides but different left-hand-sides.

in card-pyramid parsing there is significant overlap between the two sub-card-pyramids under a given node and this overlap needs to be consistently labeled.


While binary classifiers are evaluated in terms of F 1 measure, for multi-class classifiers we show the final accuracy.

for j ∈ M , c i r has a production shared by c j n 1 and c j n 2 .


This structure reflects the fact that the parser is having a hard time coordinating "heavy" NPs, where the head nouns are modified by PPs.

lastly, the structure JJ←NN←NNS←, deviates from the the "standard" NP construction, and is somewhat hard for the parser (39 times parser, 67 in language).


The most intense participation was on Task1B.

the shared task addressed the detection of uncertainty in two domains.


The latter task falls within the scope of semantic analysis of sentences exploiting syntactic patterns, as hedge spans can usually be determined on the basis of syntactic patterns dependent on the keyword.

the whole phrase is speculative therefore it is marked as a hedge cue.


These instances with 24 non-binary features carry the positive class label if the sentence is uncertain.

the classifier is the decision tree IGtree as implemented in tiMBL (version 6.2) 5 , a fast heuristic approximation of knn, that makes a heuristic approximation of nearest neighbor search by a top down traversal of the tree.


This seems to underpin the findings of previous studies that hedge cue learners appear quite sensitive to text type ( Table 3: Averaged 10-fold cross-validation results on the articles in the official shared task training data, always including the abstracts in the training portion.

first, the scope of the sentence is set to default scope.


2 Architecture and Set-Up To ease integration of annotations across system components, we converted the XML training data to plain-text files, with stand-off annotation linked to the raw data by virtue of character start and end positions (dubbed characterization in the following).

the feature types are further described in Section 3.4.


• Default -anything else Mark everything that is below the parent and after the cue.

mL1 to mL3 are variations of the system described in section 3.


We hypothesized that the speculative sentences in abstracts may differ from the ones in full papers and a 10-fold cross-validation of the development data supported this intuition.

the output of the classifier is a modified sequence of FILO tags.


As the first step, manually written rules are applied that find the scope based on GRs and POS tags.

we compare all system predictions to the list of known cues and if there is no match, they are removed.


The additional annotation has been generated by ETL based systems Fernandes et al., 2009b;Milidiú et al., 2008).

the hedge scope is the uncertain statement which is hedged by the cue.


We divide entity identification into three token classification tasks, one for each entity type.

this involves a special modeling issue: interdependent classification.


We simply determine the phrasal node that dominates the hedging cue and consider the tokens within that phrase as being in the scope of the cue, unless they meet one of the following exclusion criteria: 1.

we explicitly targeted precision, and in that respect, our submission results were not surprising.


Uncertainty as a target category needs to be addressed with some care.

the learning problem then amounts to finding parameters w such that S(x, y x ) ≥ S(x, y) + ∆(y x , y) for all y ∈ Y x \ {y x } over the training data D. we want the score of the correct label to be higher than the score plus the loss, of all other labels, for each instance.


The strings ahead of scope head and tail not listed are also counted, but they do not give such valuable information as the two items listed in Table 4.

rule 2: if none of scope head or tail is predicted, the head is set to the word just before the cue words; the tail is set at the end of the sentence.


CRF model can avoid the label bias problem of HMMs and other learning approaches.

when the CRF model gives low confidence, we just set the most probable positions of scope head and tail.


We use the following rules to find the scope.

for sentence (a) in Section 1, the classifier assigns f-scope to indicate, L-scope to benchmarks, and None to the rest of the tokens.


• If the POS of the hedge cue is attributive 3 Available at http://nlp.stanford.edu/software/lex-parser.shtml adjectives, the L-scope is signed at the following noun phrase.

we need to process the output of the classifier to get the complete sequence of the scope.


We use SpanNear-Query and BooleanQuery to search the matched sentences for hedge cues.

its architecture is presented in Figure 4.


We firstly tried to use the token as the basic unit for hedge cues.

lucene provides various types of queries to search the indexed content.


Uncertainty classification involves partitioning the set of sentences in a dataset into certain and uncertain classes.

section 4 describes the evaluation of the system and section 5 discusses the results.


Hedge detection is largely based on the identification of lexical items like suggest and might which indicate sentence level uncertainty.

we can then use the large amounts of unannotated sentences that are available to extract n-gram features that have high uncertainty class conditional probability and add them to our training set with those features labeled as hedges as described in Medlock and Briscoe (2007).


Our system produced the worst performance on the Wikipedia data although this was mostly due to a drop in precision compared to the biomedical domain.

hedgehunter uses both lexical and POS features for classification.


After applying the subtree extraction, the subtree can contain more word tokens in addition to those contained in the n-span, which are replaced by a common symbol.

the presence of a speculative keyword not always conveys a speculation assertion which makes the speculation detection a tough problem.


CRFs proved to marginally improve the prediction accuracy while substantially improving the speed.

our approach was to explore the use of deep syntactic features in this tagging task.


Section 5 describes the system used for the shared task final submission and presents the results obtained in the evaluation.

the performance improvement is due to the improved recall, which is as per the expectation that syntactic features would help identify new patterns, which lexical features alone cannot.


Error analysis shows that inclusion of features involving F 9 affects prediction of "believe", "possible", "putative", "assumed", "seemed", "if", "presumably", "perhaps", "suggestion", "suppose" and "intriguing".

as a part of preprocessing, words in each input sentence are tokenized using the GENIa tagger 2 .


We would also like to thank Mr. Ono, Mr. Yonetsuji and Mr. Yamada for their contributions to the library.

detecting hedge cues that indicate the uncertainty of the statement is an important subtask of information extraction (IE).


We were surprised about the large difference in performance of the model on the two data sets.

we estimated the probability of each training data word appearing in a hedge cue with unigram statistics: where P (w in cue) is the probability that word w appears in a hedge cue, f (w) is frequency of the word w in the data and f (w in c) is the frequency of the word inside hedge cues.


Sentences of the uncertain class contain one or more words which have been marked as indicator of uncertainty, the so-called hedge cues.

similar performance differences were reported by other participants in the shared task, so they seem data-related rather than being an effect of the chosen model.


In our system, we have combined CRF++ 6 and OpenNLP MaxEnt 7 classifiers into an ensemble classifier.

• parent_lemma: the lemma of the parent token of the current token.


(2009), we use the BIO-cue labels for all tokens in a sentence to predict whether a token is the first one of a hedge cue (B-cue), inside a hedge cue (I-cue), or outside of a hedge cue (O-cue).

our experiments are conducted on two training data sets: one is the abstracts and full articles from BioScope (biomedical domain) corpus (Vincze et al., 2008) 1 , the other one is paragraphs from Wikipedia possibly containing weasel information.


Lexicalization We lexicalize only in the second (full data) stage, using the method of Headden et al.

disallowing punctuation as a parent of a real word made things worse, suggesting that not all marks belong near the leaves (sentence stops, semicolons, colons, etc.


It might be that letters, or phones, or morphemes, or syllables, or something altogether novel maximize chunkiness scores.

as mentioned in Section 4.3, computational evaluation has demonstrated repeatedly that TP provides a poor model of segmentation ability in natural language.


During Cause assignment, we add features representing whether the trigger has been assigned a protein as a Cause and the candidate Cause is an event trigger.

we extract the shortest unlexicalized dependency path connecting each trigger-argument pair, allowing the paths to follow either dependency direction.


Automatic extraction of opinion holders and targets (together referred to as opinion entities) is an important subtask of sentiment analysis.

in the kernel, however, for each combination (see table 6) the similarity matching function that computes the number of similar features remains the same.


Hence KID 4 has almost 20% recall gain over the baseline (SK baseline).

what makes our approach unique is that we use the power of sequence kernels to simultaneously identify opinion holders and targets in the Urdu language.


(2002), Mukherjee and Liu (2010)), we conjecture that the reported performance might be overly optimistic under scrutiny due to non-stylistic factors such as topic bias in gender that can make the gender detection task easier.

token-based language models are likely to learn topical words and phrases, and suffer when the topics change dramatically between training and testing.


The quality of these classifiers is important, because we will exploit them for contextual opinion analysis.

we utilize the same machine learning features as in , which are commonly used in word Sense Disambiguation (wSD).


The teacher randomly decides whether or not to use the correction as its utterance according to the correction probability.

this suggests re-visiting discussions in linguistics that assume that the learner must identify teacher corrections in order for them to have an influence on the learning process.


With feature feedback, we hope to filter out some of this noise and improve the model.

with larger feature space, feature feedback can help to identify a few relevant features.


in this work, we only focus on using feature feedback as an alternative to labeled data, i.e., to provide evidence about features in terms of their relevance and class association.


Prior work (Raghavan et al., 2006;Zaidan et al., 2007) has demonstrated that users are able to reliably provide useful feedback on features.

with very large training set, the benefit from feature feedback can be expected to be small and not significant for all problems and all problems will look similar.


As with POS tagging, we expect that language model representations improve performance on the IE task by providing informative features for sparse word types.

this research was supported in part by NSF grant IIS-1065397 and a Microsoft New Faculty Fellowship.


NLP systems often rely on hand-crafted, carefully engineered sets of features to achieve strong performance.

and by careful design of the language models, we can limit the number of features that they produce, controlling how sparse those features are in training data.


Expressing the lattice model in log-linear form, we can write the marginal probability P (x) of a given sentence x as: for y i,j = s, y i,j+1 = s , and Computationally, the lattice MRF is preferable to a naïve Factorial HMM (Ghahramani and Jordan, 1997) representation, which would require O(2 M ) parameters for an M -layer model.

we use an open source Conditional Random Field (CRF) (Lafferty et al., 2001) software package 4 designed by Sunita Sarawagi and william w. Cohen to implement our supervised models.


In this dataset, there are 72, 900 unique words in documents, and 12, 405 unique words in keyphrases.

given a document, TextRank first builds a word graph, in which the links between words indicate their semantic relatedness, which are estimated by the word co-occurrences in the document.


4, we find that more translation pairs result in better performance.

for WAM, we only show the results using title-based pairs prepared with split method.


(2001), Schwartz and Hearst (2003) and Nadeau and Turney (2005), disambiguate the acronym by comparing its letters di-rectly to the initial letters in the possible long-forms and, therefore, would have difficulties in distinguishing between acronyms with similar long-forms (e.g., RA referring to Refractory anemia or Rheumatoid arthritis).

the first-order features in the instance vector contain the words from the entire abstract.


In these plots, the y-axes denote the φ-edge ratio of the constructed graphs.

we use two most frequently used classification algorithms: Gaussian Random Fields (GRF) (Zhu et al., 2003) and the Local/Global Consistency algorithm (LGC) (Zhou et al., 2004).


This latter case is the focus of this paper.

table 1 shows the classification accuracy of GRF on the two graphs.


Finally, Table 3 evaluates the effectiveness of the cut strategy exemplarily on one of the tasks.

the models formalize the probability of obtaining the foreign sentence from a given English sentence, by considering hidden variables called alignments: Here, the subscripts d and l denote two sets of parameters: whereas the set l defines the probability of an alignment without knowing any of the sentences, d describes the translational probability given an alignment and a source sentence.


The model can be inferred from the data using Gibbs sampling, as outlined in (Griffiths and  -an approach we follow in our experiments.

hence, we decided to use only one sample in our experiments.


Indeed, Figure 2 shows that this is the case (the differences between the best Topic SVM results and the best LDAH-M results are statistically significant).

dixon's texts are randomly split into 5 or 3 subsets, depending on the other author (Rich or McTiernan respectively), and the base classifiers are trained on each subset of dixon's texts together with all the texts by the other judge.


It is also only with these resources combined that we are able to outperform the MFS baseline, and we do so by 2.78%.

in these graphs, nodes represent synsets, as opposed to instantiating separate nodes for different members of the same synset and allowing edges to be constructed between them.


(2009), who created disjoint training sets for building two initial classifiers and successfully identified opinion sentences in camera reviews by selecting auto-labeled sentences agreed upon by both classifiers.

target domain news articles We used 9,500 labeled movie review sentences to train a Naïve Bayes classifier for news articles.


Wiebe and Riloff (2005) used a selftrained Naïve Bayes classifier for classifying subjective sentences and achieved better recall with modest precision over several rule-based classifiers.

we removed a small number of stop words.


The loop is repeated until convergence or a fixed number of iterations is reached.

each triple encodes a spatial relation between two objects in the following format: <<adj1, obj1>, prep, <adj2, obj2>>.


For brevity, we retain the original order of words in the given triple.

there is larger inherent variability in generating sentences from images than translating a sentence from one language to another.


Because future states can provide additional and valuable information for ambiguity resolution, improved accuracy is expected.

first, we obtain the following relation: ||w k+1 • u|| 2 = ||w k+1 || 2 ≤ (kδ) 2 -(1).


Substituting this into (2) gives ||w k+1 || ≤ (R 2 + 2C)/δ -(3).

we assume the existence of a weight vector u (with ||u|| = 1), δ (> 0), 5 S * c and S * depend on the weight vector at each point, but we omit it from the notation for brevity.


We also attempted to apply the HDLR algorithm.

its NDCG scores are also consistently higher across all dimensions.


In our experiment, when the task is a binary classification problem, pairs labeled as same, subset, or superset are considered relevant, and pairs labeled as disjoint are considered irrelevant.

the output of each concept node is a linear com-bination of the weights of all the terms in the original term vector.


Such an issue is more severe in cross-lingual settings.

we initialized the models of S2Net and HDLR using the PCA matrix.


The pre-trained model had also used a somewhat different catalog of name types, which did not include the OntoNotes NORP type (for nationalities, organizations, religions, and political parties), so that category was never predicted.

tables 22 and 23 summarize the approaches of the participating systems along with some of the important dimensions.


This information could be automatically deduced, but since it would add additional complexity to the already complex task, we decided to provide oracle information of this metadata both during training and testing.

• Scoring coreference seems to remain a significant challenge.


Following the recent CoNLL tradition, participants were allowed to use both the training and the development data for training the final model.

maintaining format consistency with earlier CoNLL tasks was deemed convenient for sites that already had tools configured to deal with that format.


For example, our system generates the predicted mention, the working meeting of the "863 Program" today, for the gold mention the working meeting of the "863 Program".

the semantic sieves do not help (row 3 versus 4).


As a result of the singleton mentions not being annotated, there is an inconsistency in the sample distributions in the training and inference phases.

table 1 shows the performance for the two inference protocols, with and without constraints.


On the mention level, we considered the head word (HD) of the mention, and following the edges in the dependency tree, we considered the left-most and right-most children of the head word (HDLMC and HDRMC), the left and right siblings of the head word (HDLS and HDRS), as well as the governor 1 of the head word (HDGOV).

the features marked with a dagger † originate from the Soon et al.


Ideally, we would have generated a complete bigram set from our features.

we included these features in our pool of feature templates, but we could not see any contribution from them during the feature selection.


Distance and Position: Distance between m i and m j in sentences; Distance in number of mentions; Distance in number of person names (applies only for the cases where m i and m j are both pronouns or one of them is a person name)*; One mention is in apposition to the other (y/n).

for the gold mention boundaries task, we were not able to assess system performances on the development set.


In the current version, our system does not consider verbs when creating candidate mentions.

we depict the system architecture and present experimental results and findings of our participation in the CoNLL 2011 shared task.


The mentions contain only first or second person pronouns.

in this paper we present an end-to-end coreference resolution system, COPA, which avoids the division into two steps and instead performs a global decision in one step.


Our current mention detector does not extract verb phrases.

the preprocessing models are not trained on CoNLL data, so we only participated in the open task.


COPA assigns edge weights by applying simple descriptive statistics on the tranining data.

it misses all the Event mentions in the OntoNotes corpus.


The first solution that intuitively comes to mind is binary classification of markable pairs (links).

classification of links to coreferent/disreferent.


To simplify notation, features which apply to the pair m j ,m k take m k as an implicit parameter.

if it does not, or if the corresponding n th schema role has fewer than two participants in c j , the value of this feature is N. Our system was implemented in Python, in order to make use of the NLTK library 2 .


Most often this information is used in the form of relationships between words -for example, how semantically similar two words are, or which nouns are the objects of a verb.

surprisingly, Rahman and Ng (2011) demonstrated that a system using almost exclusively lexical features could outperform systems which used more traditional sets of features.


For identifying an antecedent for a pronoun we consider all the noun phrases before the pronoun in the current sentence and in the four sentences preceding the current sentence.

the antecedent and pronoun category agrees.


When a coreferring mention M c has been found, a vector is constructed for every pair of mentions M k , M i , where M i is a mention that has been visited, including the coreferring one.

we describe the heuristic used to select the pairs of coreference candidates that are feeded to the network for training, and our feature selection method.


We finally chose the MLP models for the test evaluation due to its better performance on the predicted dev-set.

• Sent : this indicates the amount of sentences marker (like .


Entities are generated by the semantic module by drawing a type t according to φ, and then using that type's multinomials to populate word lists for each property.

these changes led to improvements in coreference F-score of 10.06, 5.71, 6.78, 6.63 and 3.09 on the MUC, B 3 , Ceaf-e, Ceaf-m and Blanc, metrics, respectively, and a final task score of 47.10.


Using an oracle to exclude all spurious mentions at evaluation time yielded improvements ranging from five to thirty percent across the various metrics used in this task.

affinities between mentions are defined by a log-linear model with parameters π for a range of standard features.


The transitivity constraints are formulated as These constraints ensure that when any two corefrent links (e.g., Ü , Ü ) among three mentions exist, the third one Ü must also be a link.

for cascaded model, we take mentions identified in first phase as inputs for coreference resolution.


We also experimented with instance sampling in order to reduce the bias towards non-coreference in the training set.

in section 3, we present UBiU's results on the development set, and in section 4, UBiU's final results in the shared task.


Since singleton mentions are not annotated in the OntoNotes data set, mentions without coreference relations after classification need to be removed from the answer set, which can only be performed after coreference resolution when all coreferent pairs are identified.

for this reason, many systems use superficial information such as string match.


The results show that overall the use of gold standard mentions results in an increase of the average F-score of approx.

this leads to a drastic decrease in system performance for two reasons.


Reconcile (Stoyanov et al., 2010b) is a general coreference resolution research platform that aims to abstract the architecture of different learningbased coreference systems and to provide infrastructure for their quick implementation.

we used the Reconcile-internal versions of scorers to optimize the threshold.


We slightly modify the conjunction scheme and expand it with additional backed-off conjunctions, since these changes lead to features that empirically transfer better than the MSTParser defaults.

only two query types are shown here, but values are computed for many others as well.


The projection directions learnt during the training are used to find the transliteration for a test name as described in Sec.

if we want to build a transliteration system between every pair of languages in a given set of languages then these approaches need resources between every pair of languages which can be prohibitive.


The seed distributions are propagated over a graph representing relations among words, and translation pairs are extracted by identifying word pairs with a high similarity in the seed distributions.

to obtain co-occurrence relations with all the seeds, including indirect relations, we focus on a graph-based label propagation (LP) technique (Zhu and Ghahramani, 2002).


The first is a method that finds translation pairs in parallel corpora (Wu and Xia, 1994;Fung and Church, 1994;Och and Ney, 2003).

in summary, our proposed method consists of three steps (see Algorithm 1): (1) graph construc-Algorithm 1 Bilingual Lexicon Extraction Require: comparable corpora D e and D f , a seed lexicon S consists of S e and S f Ensure: Output translation pairs T 1-1: G e = {E e , V e , W e } ← construct-graph(D e ) 1-2: e , W e , Q e } ← propagate-seed (G e , S e ) 2-2: tion for each language, (2) seed propagation in each graph, (3) translation pair extraction.


We will leave this word sense disambiguation problem for future work.

a preliminary examination showed that the NTCIR parallel data covers less than 3% of all words because there are a number of technical terms and neologisms.


This is why translation pairs could have wrong seed distributions and then the proposed methods could not identify correct translation pairs.

we used 1,479,831 Japanese documents and 438,227 English documents.


We perform our experiments on the 201 transcripts of the 39 speakers.

when talking about sad experiences, patients sometimes show satisfaction and insistence, while the controls talked more about working experiences.


We tried unigram, bigram and trigram language models by word and POS tag.

for each feature j, we have: Here we simply normalize all feature values to a range of [0, 1], where 0 corresponds to the smallest observed value and 1 to the largest observed value across all transcripts.


Unfortunately the sign of an interaction cannot be trivially inferred from the polarity of sentences.

dependency features were most helpful, but other types of features helped improve the performance as well.


The sequence of words connecting the two entities is a very good predictor of whether they are related or not.

we also notice that most negative edges are between groups, and most positive edges are within groups.


The Topic Goal model explains this much more easily by grouping commonly co-occurring values into the same topic.

our upper bound does not include such a term.


Since Baseline 1 barges-in on users after an information peak, when ID may still be high, it continuously receives a negative reward reflecting the user preference for late barge-ins.

the goal of each SMDP is to find an optimal policy π * that maximises the reward for each visited state, according to where Q i j (s, a) specifies the expected cumulative reward for executing action a in state s and then following π * .


59% of the time (138 utterances, 1tailed Sign test, p < 0.025).

the agent learns to barge-in right before information density peaks in an incoming user utterance to clarify or request slots that are still open from the previous information density peak.


Our domain-adaptation strategy builds an Open-DB NED system by training it on labeled examples from an initial database or small set of initial databases.

differences between the Modified Zhou Wikifier and the Open-dB strategies are statistically significant (p < 0.01, Fisher's exact test) on both domains.


Once trained, it is ready for NED over any database that meets our assumptions, out of the box.

wikipedia is clearly a highly valuable resource, but it should not be thought of as the only one.


In order to allow our systems to link to Wikipedia, we create a single "document similarity" feature describing the similarity between the text around a mention and the text appearing on a Wikipedia page.

if we use µ partial for computing our models' feature functions, like the Count Att(m, s) in the domain adaptation model, counts varied widely across domains.


While induction in Aleph was performed with the same target language as in our approach, the target language of ALP is JAPE, which has been shown (Chiticariu et al., 2010b) to lack in some of the constructs (such as minus) that AQL provides and which form a part of our target language (especially the rule refinement phase).

the formal definition of these operators takes the form of an algebra (Reiss et al., 2008), similar to relational algebra, but with extensions for text processing.


Existing approaches to rule induction for IE focus on rule-based systems based on the cascading grammar formalism exemplified by the Common Pattern Specification Language (CPSL) (Appelt and Onyshkevych, 1998), where rules are specified as a sequence of basic features that describe an entity, with limited predicates in the context of an entity mention.

the induced CR rules are too fine-grained.


The select statement is similar to the SQL select statement but it contains an additional consolidate on clause (explained further), along with an extensive collection of text-specific predicates.

advanced constructs such as dynamic dictionaries are not supported, and the set of predicates to the Filter construct supported in our system is restricted to predicates over other concepts, which is only a subset of those used in (Chiticariu et al., 2010b).


Based on different sample selection strategies, they can be grouped into two main categories: (1) uncertainty sampling (Lewis and Gale, 2004) where the active learner iteratively select most uncertain unlabeled samples for manual annotation; and (2) committee-based sampling where the active learner selects those unlabeled samples which have the largest disagreement among several committee classifiers.

although the certainty approach could select many MI samples for annotation, this approach performs worst due to its totally ignoring the uncertainty factor.


We call our novel active learning approach co-selecting due to its collectively selecting informative samples through two disjoint feature subspace classifiers.

in this section, we give a brief overview on sentiment classification and active learning.


Traditionally, uncertainty has been popularly used as a basic measurement in active learning (Lewis and Gale, 2004).

use the sampling strategy to select n most informative samples for manual annotation (4).


The data collection consists of four domains: Book, DVD, Electronic, and Kitchen ( Blitzer et al., 2007).

our co-selecting approach employs two feature subspace classifiers to collectively select most informative minority-class samples for manual annotation by leveraging a certainty measurement and an uncertainty measurement, and in the meanwhile, automatically label most informative majority-class samples, to reduce humanannotation efforts.


For SO-CAL labels, we simply set η SO-CAL i = η SO-CAL , where η SO-CAL is a hyperparameter.

the multi-expert prior P E (r | X, β) consists of two component distributions N 1 and N 2 .


Similar to SO-CAL, we determine the prior polarity of a phrase based on the BoO dictionary.

the lexicon-based methods of (taboada et al., 2011;Qu et al., 2010) also predict ratings at the phrase level; these methods are used as experts in our model.


A number of lexicon-based approaches for phraselevel rating prediction has been proposed in the literature (Taboada et al., 2011;Qu et al., 2010).

sentence ratings should be close to document ratings unless strong evidence to the contrary exists.


If Sen bef expresses the polarity polar, then P olarity(ac) = polar.

observed from previous work (Bloom et al., 2007;Kobayashi et al., 2004;Popescu and Etzioni, 2005), there are two kinds of common relations between the polarity words and their targets.


For a sentence s containing such a collocation c, since the in-sentence features are always ambiguous, it is difficult to disambiguate the polarity of c by using them.

meanwhile, it also brings us lots of interesting and challenging research topics, such as subjectivity analysis (Riloff and Wiebe, 2003), sentiment classification (Pang et al., 2002;Kim and Hovy, 2005;Wilson et al., 2009;He et al., 2011), opinion retrieval (Zhang et al., 2007;Zhang and Ye, 2008;Li et al., 2010) and so on.


From Table 3, we can find that among all the reviews, nearly 60% collocations only appear once.

these contexts are limited, thus the resulting polarity is insufficient to be reliable.


While the previous similarity measures are purely type-based, argument similarity integrates token-based, i.e., discourse-specific, similarity information about predications by taking into account the similarity of their arguments.

this paper focuses on the first of these tasks, henceforth called predicate alignment.


Hence, the extracted sentence pairs do not always contain gold alignments.

furthermore, the entity-based approach only investigates realization patterns for individual entities in discourse in terms of core grammatical functions.


T entails H if the meaning of H can be inferred from the meaning of T, as would typically be interpreted by people."

sentence (4.a) is still part of a coherent discourse, as a role filler for the omitted argument can be inferred from the preceding context.


p(h i |h i+1 ) is the probability of the child node h i given its ancestor h i+1 .

in our particular setting, where we use the metonymy data from SemEval 2007, the domain of the PMW is well defined: locations and companies, respectively.


this approach needs novel, manual provision of readings as well as annotated examples for each new semantic class.


For example, the verb 'whisper' conveys a specific manner of talking and will probably not appear with the adverb 'loudly', while the verb 'talk' is more likely to appear with such an adverb.

first, we suggest a novel set of entailment indicators that help to detect the likelihood of verb entailment.


Accuracy is the number of words which correctly identified their parent and Complete is the number of sentences for which the entire dependency tree was correct.

we need to have access to an attribute dictionary U which contains a k dimensional representation for each word in the corpus.


The first issue this model faced was a massive parameter space and intractable inference.

we denote this a weak initialisation as no alignment models outside of those included in the PYP-SCFG model influence the resulting word alignments.


Indeed, the outputs of the two systems are not significantly different under ROUGE (using a paired t-test, p > 0.5).

note that the scores in the objective are for each tree node and not each sentence.


• ILP: This indicates the method proposed by McDonald (2007) for maximizing the score function (14) with integer linear programming.

we describe how to adapt the extractive approach to the problem of reinforcement learning in the sections that follow.


This score function was determined by reference to McDonald (2007).

iLP has to take into account the combinations of textual units, whose number increases exponentially.


We used sentences as textual units for the extractive approach in this research.

we would have to consider the range of applications with the combined method.


In a real out-of-domain task, we are unlikely to have any parallel data to tune weights for the new phrase table.

our algorithm alternates between the two cases.


The corpus contains news articles from different news agencies and has a much larger vocabulary compared with the Transtac corpus.


This paper explores document-level SMT from the tense perspective.

the vertical axis indicates different tense: 1 to "past", 2 to "present", 3 to "future" and 4 to "UNK".


Then, we explain how we build, decode, and improve the confusion network.

there is only 50% overlap at the word level, but the actual vocabulary overlap is much higher, e.g., there is only one word in the Malay text that does not exist in Indonesian: samarata ('equal').


Note also the effect of repetitions: good word choices are shared by many n-best sentences, and thus they would have higher probabilities compared to bad word choices.

translating from Arabic dialects to Modern Standard Arabic requires, among other things, normalizing informal text to a formal form.


Our parser choose the auxiliary verb as the head and the main verb and all its dependants become its children.

sentence, so the head word of the sentence is always its right dependent.


As we can see, projective buffer transitions achieve better precision than standard transitions, but this is not surprising since they act only on "easy" arcs of length 1.

as can be seen in the table, even though the pseudo-projective parser is able to capture nonprojective syntactic phenomena, the algorithm with projective buffer transitions (which is strictly projective) outperforms it in four of the eight treebanks, including non-projective treebanks such as the German one.


• Acyclicity: there are no directed cycles in G. • Node 0 is a root, i.e., there are no arcs of the form (i, l, 0) in A.

transition-based parsers (Yamada and Matsumoto, 2003;Nivre et al., 2004), which are the focus of this work, use local training to make greedy decisions that deterministically select the next parser state.


This is because adding label tuple features would introduce a large constant factor of O(|L| 3 ), where |L| is the size of the label set L, into the complexity for exact thirdorder parsing.

we again see that approximate decoding with rich features and cube pruning has higher accuracy than transition-based parsing with a large beam.


Furthermore, if the list of k language model states are sorted from the lowest cost to the highest cost, we can assume the best combinations will still be among the combinations of the top items from each list, although the incorporation of n-gram features breaks the monotonic property of the underlying semi-ring.

because our higher-order dependency parsing algorithm is based the Eisner algorithm, it is currently limited to produce projective trees only.


The algorithm stops when k items have been popped off from the queue.

it is equivalent to the first-order model from Table 1.


bels in the Penn2Malt label set, which results in little non-structural ambiguity.

the previous n modifiers, either horizontal or vertical, is a potential signature of parsing history.


Suppose we were lucky enough to fully observe the sequence of named entity tokens y i = (e i , w i ) produced by our generative model.

if it chooses to edit, it chooses a random edit type with some probability conditioned on the next input character.


For the present experiments, we designed a moderately simple one that employs (1) conditioning on one character of right context, (2) latent "edit" and "no-edit" regions to capture the fact that groups of edits are often made in close proximity, and (3) some simple special handling for the distribution conditioned on the root p(w y | ♦).

-other things equal-Barack Obama is more plausibly a variant of Barack Obama, Jr. than of Barack Obama, Sr. (which has fewer tokens).


Our result points a way towards a direction for explaining of this phenomenon by demonstrating that the differences between currenttechnology artificial speech and natural speech can be partially explained through higher-level syntactic features.

baseline model Fixed effects of the fitted model are shown in Table 2.


both A1-1 and A1-2 would contain sentiment expressions with the same polarity as that of Q1.

this indicates the performance upperbound in this experiment.


(2007) used lexical-conceptual templates for query generation.

semantic coherence is discussed in more detail later in this section.


We observe the performance drops dramatically when any parameter (i.e.

the main contributions of this paper include, • We propose to exploit the hierarchical organization of consumer reviews for answering opinion questions on products.


Compared with the global training method which tries to fit a single weight on the training data, the local one learns weights based on the local neighborhood information for each test example.

further, compared to the retrieval, the local training is not the bottleneck.


In the algorithm the parameters corresponding to developing sets are omitted for simplicity.

after the introduction of related works in section 4, we give the experimental results on Chinese word segmentation in section 5.


The transformation classifier is used to process the source corpus, with the classification label derived from the segmented sentences as the guiding features, so as to relabel the source corpus with the target annotation guideline.

it is valuable to conduct experiments with more source-annotated training data, such as the SIGHAN dataset, to investigate the trend of improvement along with the increment of the additional annotated sentences.


In combining the dictionaries, a given OOV word can be listed with different standard forms in different dictionaries.

instead of using the small 10 million tweet corpus, from this step onwards, we use a larger corpus of 80 million English tweets (collected over the same period as the development corpus) to develop a larger-scale normalisation dictionary.


The encouraging results shift the focus to reranking and promoting the correct normalisation to the top-1 position.

namely, they are string subsequence kernel (S-dict, n =40,000), double metaphone edit distance (DMdict, n =10,000) and default contextual similarity without re-ranking (C-dict, n =10,000).


Dictionary lookup is a type-based approach to normalisation, i.e., every token instance of a given type will always be normalised in the same way.

we also compare each dictionary with the performance of the manually-constructed Internet slang dictionary (HB-dict) used by Han and Baldwin (2011), the small automatically-derived dictionary of Gouws et al.


Many of these approaches (Kate and Mooney, 2007;Chen and Mooney, 2008;Chen et al., 2010) disambiguate the data and match NL sentences to their correct MR by iteratively retraining a supervised semantic parser.

1 our method, which uses correspondences from the LHG at the semantic concept level, constructs a more focused PCFG of tractable size.


Experiments are carried out on large scale English-to-Japanese and Chinese-to-English translation tasks, and significant improvements are reported on both translation quality and alignment quality.

2The second uses the n-best FDTs of each sentence pair, which is motivated by several studies (Venugopal et al., 2008;Liu et al., 2009).


The descent of the word alignment quality will lead to inaccurate component models straightforwardly.

figure 3 gives an example of extracting a rule sequence from an fDT.


First, they use a deterministic model that aggregates latent instance labels into a set of labels for the corresponding tuple by OR-ing the classification results.

bunescu and Mooney (2007) and Riedel et al.


Each classifier outputs p(z|x i , w z ) for tuples in a given fold during the E-step (equation 2) and is trained (equation 3 i , w z ) in equation 5as the average of the probabilities of the above set of mention classifiers: z are the weights of the mention classifier responsible for fold j.

we can still use EM, but the optimization focuses on maximizing the lower bound of the loglikelihood, i.e., we maximize the above joint probability for each entity pair in the database.


It is worth mentioning that the quality of the predictions is not the same across the levels, as shown in Table 5.

these formulas only take into account superficial features, ignoring other important aspects contributing to text difficulty, such as coherence, content density, inference load, etc.


Thirteen predictors were implemented to account for the number or the frequency of the orthographic neighbors of all words in a text.

it seemed unlikely that the information collected by means of syntactic parsers, which are still committing a significant number of errors, at least for French, would belie these findings.


The maximum scoring directed spanning tree can be found efficiently when the score of a tree depends only on edge-based factors (McDonald et al., 2005b).

within a gap-minding tree, consider any node n with a gap (i.e., n's projection forms two non-contiguous intervals Let p be the parent of n. 1.


By Claim 1, we can split up the score of the tree as the score of the edge from p to x (Score(p, x)), the score of the subtree corresponding to the projection of x to the left of its gap (C[i, k, x]), and the score of the subtrees rooted at p with its remaining children and the subtree rooted at x corresponding to the right side of x's projection (D[k + 1, j, p, x, T]).

under these syntactic assumptions, we would expect at most one child to inherit a parent's gap.


The relations annotated are both intra and inter-document, which more accurately models real-world scenarios.

most of this prior work focused only on event coreference, whereas we address both entities and events with a single model.


Though syntax parsing model can directly predict the POS tag itself, we choose not to use this, but use an independent POS tagger for two reasons.

all the patterns are ranked in descending order of the reduction/increase of the error number.


Hence for comparison we trained another parser using automatically generated POS tags obtained from 10-fold cross validation, but did not find significant difference between these two parsers when testing on the perfectly segmented development dataset.

output: Word sequence, PoS tag sequence, and parse tree 0: ∀0 ≤ i ≤ j ≤ l − 1, calculate score seg (x, i, j) using Equation 11: For each character boundary b i , 0 ≤ i ≤ l, get the best previous and next words of b i using bidirectional Viterbi decoding 2: ∀0 ≤ i ≤ j ≤ l − 1, t ∈ T , calculate score seg⊕pos (x, i, j, t) using Equation 23: ∀b i , 0 ≤ i ≤ l, t ∈ T , get the best PoS tags of words left/right to b i using bidirectional viterbi decoding.


We used the same preprocessing step as (Harper and Huang, 2009), collapsing all the allowed nonterminal-yield unary chains to single unary rules.

the basic idea of our decoding algorithm is to extend the CYK parsing algorithm so that it can deal with transition features in POS tagging and segmentation scores in word segmentation.


The best published system result on CTB5 is Petrov and Klein's, which used PCFG with latent Variables.

the right hand side of each rule should contain no more than 2 states.


While traditional Information Extraction (IE) (ARPA, 1991;ARPA, 1998) focused on identifying and extracting specific relations of interest, there has been great interest in scaling IE to a broader set of relations and to far larger corpora (Banko et al., 2007;Hoffmann et al., 2010;Mintz et al., 2009;Carlson et al., 2010;.

for comparison, we use a state-of-the-art SRL system from Lund University (Johansson and Nugues, 2008), which is trained on PropBank (Martha and Palmer, 2002) for its verb-frames and NomBank (Meyers et al., 2004) for noun-frames.


Our algorithm first checks for the presence of a ccomp edge to the relation node.

oLLIE increases precision by reducing confidence in those extractions or by associating additional context in the extractions, in the form of attribution and clausal modifiers.


Similarly, we may conclude (Annacone; is the coach of; Federer) from the sentence "Federer hired Annacone as a coach.

the requirement of having pre-specified relations of interest is a significant obstacle.


Also, contributions from individual data z i have been lost so the mixing of the MCMC can sometimes be slow.

results for "Moby Dick" on chapters are comparable.


The second one is paraphrase detection, i.e., the task of examining two sentences and determining whether they have the same meaning (Socher et al., 2011a).

analogously, in Section 2.2 we consider three methods of vector composition, i.e., how a phrase or a sentence can be represented as a vector using the vectors of its constituent words.


Some approaches rely on k-order generative probabilistic models, such as HMMs (Molina and Pla, 2002).

to tackle the problems with the sequence labeling approaches to phrase chunking, we formulate it as a joint problem, which maps a Chinese sentence x with segmented words and POS tags to an output y with tagged chunk types, like the S1 in Example 1.


(2) into line 4 of the algorithm in Figure 2, we obtain k-best MIRA.

the gap between the two systems is 27.73% when the length hits 4.